65

Vegetation Fires in the Asian Region:
Satellite Observational Needs and Priorities
Krishna PRASAD VADREVU* and Christopher O. JUSTICE
Department of Geography, University of Maryland,College Park, Maryland, USA*e-mail: krisvkp@umd.edu

Abstract
Fire plays important role in shaping ecosystem structure and function. Depending upon the complex
effects of fire, it can have either beneficial or harmful effects. Remote sensing technology with its synoptic,
multi-temporal, multispectral and repetitive coverage capabilities can provide valuable information on firecounts, radiative power, area burned and vegetation type. In this article, we briefly review the potential ofsatellite remote sensing data for mapping and monitoring vegetation fires. For describing fire events in Asia,
we used nine years of MODIS datasets in conjunction with the MERIS 300m vegetation map. In several
regions of the world, including Asia, scientists are looking forward to an improved understanding of the utilityof satellite products for resource management and policy. To meet such requirements, an international
program called Global Observation of Forest Cover/Global Observations of Land Dynamics (GOFC-GOLD)has been coordinating forest, land and fire activities across the globe. More specifically, the GOFC-GOLD
Fire Implementation Team (IT) has been involved in articulating the use of satellite-derived fire products andinformation from existing and planned systems for global change research, fire management and policy
decision-making. Accordingly, a brief description of GOFC-Fire IT’s activities, goals and strategies isprovided. Satellite observational needs and priorities for effective fire monitoring in the Asian region are also
discussed.
Key words: Asia, GOFC-GOLD, MODIS data, vegetation fires

1. Introduction
Vegetation fires across the world, including the Asian
region, have received increasing attention because of awide range of ecological, economic, social and political
impacts. The role of fire in the creation and maintenanceof landscape structure, composition, function and ecological integrity has been well recognized (Goldammer,
1990; Pyne et al., 1996; Roberts, 2000; Saha, 2002;
Dennis et al., 2005). At the local scale, fire can stimulate
soil microbial processes and combust vegetation, ultimately altering the structure and composition of bothsoils and vegetation (Agee, 1994; De Bano et al., 1998).
Also, at the regional and global scales, combustion offorest and grassland vegetation releases large volumes ofradiatively active gases, pyrogenic aerosols, and otherchemically active species that significantly influence theEarth’s radiative budget and atmospheric chemistry(Andreae & Merlet, 2001), impacting air quality (Hardyet al., 2001) and risks to human health (Brauer, 1999;
Sastry, 2000).
Historically, in the Asian region, fires were thought tobe confined to open deciduous forest and savanna forest
Global Environmental Research
15/2011: 65-76
printed in Japanecosystems (Johnson & Dearden, 2009). However, satellite remote sensing data as detected from the MODIS
sensor reveal that fires occur over a variety of geographical regions (Fig. 1) covering diverse ecosystems
including tropical evergreen forests, closed deciduous
forests, mixed forests, scrub lands, agricultural areas, etc.
(Fig. 2a-c; Fig. 3). Fires in the Asian region are associated with environmental and economic losses, including
health hazards. For example, recent estimates suggestthat the peat lands of Southeast Asia cover approximately
250,000 km2 (Carbopeat, 2011) and represent an immense reservoir of fossil carbon (Page et al., 2002;
Jaenicke et al., 2008). CO2 emissions from peat fires are
estimated at about 30% of global CO2 emissions (Hooijer
et al., 2006). Previously, in Indonesia, the area burned in
the catastrophic fires of 1997/98 was estimated at
9.7 million hectares of forest and non-forest land, withsome 75 million people affected by smoke, haze and thefires themselves. Impacts included damage to health, lossof life and property and reduced livelihood options. The
economic costs were estimated to exceed 9 billion USD(Barber & Schweithelm, 2000). The impacts of theresulting atmospheric pollution on health, transport and©2011 AIRIES

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K. P. VADREVU and C.O. JUSTICE

Fig. 1 MODIS-derived active fires in the Asian region (2005).

(b)

(a)

(c)
Fig. 2 MODIS-derived vegetation fires: a) April 8th, 2003, Burma; b) March 05, 2010 South-East Asia;c) March 4th, 2010, India and Myanmar.

Vegetation Fires in the Asian Region

Residue burning

67

Pine forest burning

Managed fires?

Vegetation Fires
MODIS
Terra/Aqua

Slash and Burn

Fig. 3 MODIS-derived vegetation fires in the Indian region.

tourism were borne largely by Indonesia, Brunei
Darussalam, Singapore and Malaysia (Radojevic, 2003).
Similarly, in India, the Himalayan forest fires during
1995 consumed nearly 6 Mha and caused huge economic
losses (Bahuguna & Upadhyay, 2002). The regular agricultural residue burning events that occur to-date in the
Punjab region of India were shown to cause enormous
pollution problems, enhancing direct radiative forcing in
the Indo-Ganges region (Ramanathan & Carmichael,
2008; Vadrevu et al., 2011). Chan et al., (2000) from
Ozonesonde profiles, satellite fire counts, CO data andback-air-trajectory analysis, showed that biomass burning emissions in Southeast Asia are a source of ozone
enhancement in the lower troposphere in springtime overHong Kong. More examples of the impacts of fires in the
Asian region and on the local environment can be foundin Smith et al. (1996); Christopher et al. (1998); Streets
et al. (2003); Akimoto (2003); Venkataraman et al.
(2006); Lau & Kim (2006); Badarinath et al. (2007);
Ramanathan & Carmichael (2008); Vadrevu et al. (2011),
etc. The causative factors of fires in the Asian region
have been mainly attributed to slash and burn agricultural
practices, agricultural residue burning to prepare for the
next crop rotation, unsustainable forestry and plantation
development including for timber products, plywood,
palm oil, etc., clearing of peat lands to establish riceproduction in Kalimantan, management of feed for
grazing animals in national parks, etc. (Schmidt-Vogt,
1998; Fox et al., 2000; Siegert et al., 2001; Stolle et al.,

2003; Gupta et al., 2004; Murdiyarso et al., 2004;
Butchiah et al., 2009). In addition, the role of climate
factors cannot be overlooked. For instance, regional haze
events in Indonesia during 1982, 1983, 1987, 1991, 1994,
1997, 1998 and 2002 were reported to be tightly coupledwith El Niño events (de Groot et al., 2006).
Accurate information on fires (location, frequency,
extent, seasonality, etc.) is necessary for making strategic
and informed decisions on fire management by local
authorities and civil protection agencies. A combinationof space- and ground-based observations are necessary
for monitoring fires. The transboundary dimensions offire impacts and air pollution necessitate organizedefforts to understand, plan and implement effective
responses. While some countries within the Asian region
have developed their own organizational setup throughwhich fire management strategies are carried out (e.g.,
India, Japan, Singapore, Indonesia, etc.) several others
lack such systems.
In this study, we briefly review the potential of satellite remote sensing data products for use in fire mappingand monitoring, followed by a detailed analysis of thelatest fire situation in the Asian region. For describing
regional fire events spatially and temporally, we use the
MODIS active fire product (Justice et al., 2011). We alsouse 300m MERIS vegetation product to assess vegetation-fire statistics over the entire region. Since successful
fire management depends on fire prevention, detection
and preemptive suppression measures, we call for an

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K. P. VADREVU and C.O. JUSTICE

increased role for regional scientists in developing, calibrating and validating fire products at a local scale
through active involvement with other international
scientific communities such as the Global Observationof Forest Cover-Global Observation of Land Cover
Dynamics (GOFC-GOLD) – Fire Implementation Team(IT). Accordingly, an overview of GOFC-Fire IT’s
activities has been highlighted along with its goals andstrategies. Satellite as well as ground-based observational
needs for effective fire monitoring in the Asian region are
also highlighted.

2. Satellite Remote Sensing of Fires
Over the past few decades, spatial information technologies have been widely used in vegetation fire studies(Justice et al., 2002; Giglio et al., 2003; Roy et al., 2005).
Remote sensing with its multi-temporal, multi-spectral,synoptic and repetitive coverage can provide valuable
information on fire counts, area burned and types of
ecosystems burned (Justice et al., 2003). Csiszar et al.,(2009) highlighted a variety of satellite products useful
for Fire research in the context of the Global Terrestrial
Observing System’s (GTOS) Essential Climate Variables.
Broadly, satellite derived fire datasets can be categorized
into three different types:
A) Active fire products: fires emit thermal radiation
with a peak in the middle infrared region, in accordancewith Planck’s theory of blackbody radiation. Therefore,
active fire sensing is often undertaken using middle infrared and also thermal infrared (usually around 3.7 to11 mm) satellite bands (Dozier, 1981). Active fire countdata from ATSR, MODIS, etc., can be used to characterize seasonal and geographic variations in fire occurrences.
Higher resolution systems such as ASTER and Landsat
7 have been used to identify active fires, albeit with lessfrequent coverage (Giglio et al., 2008).
B) Burned areas: satellite measurements in the nearand short-wave infrared are sensitive to changes in surface reflectance caused by burning. Mapping of burned
areas mostly relies on detecting changes in vegetation
indices although other more sophisticated approaches areavailable (Roy et al., 2005; Giglio et al., 2009). Currently,
the burned areas can be retrieved from LANDSAT, SPOT,AWiFS, MODIS, etc. Some of the burnt area products
derived from satellites include Global Burned Areas(L3JRC), GBA 2000, GLOBSCAR, MODIS burned area
product, GLOBCARBON, etc.
C) Fire radiative energy products: instantaneous fireradiative power (FRE) measured from middle infrared
satellite bands can be used to estimate the amountof biomass burned (Wooster et al., 2003). Recently,
Vermote et al. (2009) and Boschetti and Roy (2009)
demonstrated methodologies for calculating FRE, usefulin emissions estimation. Some of the active fire productswith FRP information include GFED, WFABBA,
MODIS FRP, SEVIRI FRP, etc.
Some of the satellites useful for fire products generation are listed and described in Fig.4. MODIS-based fireproducts can be accessed through the University of
Maryland webserver, http://modis-fire.umd.edu/Burned_
Area_Products.html.

Fig. 4 Satellites useful for fire product generation.

Vegetation Fires in the Asian Region

3. Vegetation Fires in the Asian Region
In this study, we used the MODIS active fire datasetsfrom 2002-2010 for analysis of fire events in the Asian
region. The MODIS active fire product detects fires in
1 km pixels that are burning at the time of overpass underrelatively cloud-free conditions using a contextual algorithm. It first applies thresholds to the observed
middle-infrared and thermal infrared brightness temperature and then rejects false detections by examining
the brightness temperature relative to neighboring pixels(Giglio, L. et al., 2003). Fire frequency refers to the number of fires over a given period. The aggregated yearly
MODIS fire counts for 2002-2010 for different countries
are shown in Fig. 5 and average fire counts in Table 1. Anaverage of 382,455 fire counts per year was recorded
from the Asian region. Of the different countries,
Myanmar recorded the highest number of fires followedby India, Indonesia, China, Laos, Thailand, Cambodia,
Vietnam, and others. These seven countries accounted for
92.4% of all fires in the Asian region. Macao and theMaldives recorded no fire events during the nine-year
period. Spatial distributions of fire events for the typicalfire year of 2005 are shown in Fig. 1 and monthly variations in Fig. 6. The results suggest that seventy-five
percent of all fires occur between January and May in the
Asian region, with the peak fire season in March. Further,
MODIS Aqua captured 70% of the fires relative to
MODIS Terra (Fig. 7), suggesting that most of the biomass is burned during the late afternoon.

69

For analyzing the fire occurrences in individual
vegetation categories, we used the MERIS 300 m vegetation product which was created using an automatic andregionally-tuned classification of time-series of 300 m
MERIS full-resolution mosaics from December 2004 toJune 2006 (Bicheron et al., 2008). The map consists of
22 land cover classes using the Land Cover Classification
System (LCCS) and a more detailed Level-2 legend.
Figure 8 shows a map of the Asian region. MODIS firecount data for the year 2005 were overlain on the MERIS
vegetation map and analyzed for spatial variations. Of thedifferent vegetation types, fire counts in the closed toopen shrub land category dominated at 18.8%, followed
by closed to open, broadleaved evergreen-semi deciduous forest, rain fed croplands (16.9%), post-flooded or
irrigated croplands (12.0%), mosaic cropland vegetation(10.55%), mosaic vegetation/cropland (9.98%), etc.
(Fig.9). These results clearly suggest that in addition toforest fires, agricultural fires in the Asian region are also
important to consider for addressing fire management
and mitigation options.
To locate geographical regions of high fire incidences,a hotspot analysis was carried out using the K-meansclustering algorithm. The K-means clustering algorithm
partitions ‘n’ observations into ‘k’ clusters, in which eachobservation belongs to the cluster with the nearest mean.
It is similar to the expectation-maximization algorithm
for mixtures of Gaussians (MacQueen, 1967; Kanungo
et al., 2002). The spatial clusters delineated using theK-means algorithm are shown in Fig. 10.

Fig. 5 Vegetation fire statistics in the Asian countries derived from MODIS datasets

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K. P. VADREVU and C.O. JUSTICE

Table 1 Annual average fire occurrences (active fires) indifferent countries derived from nine years of MODIS
Active Fire data (2002-2010).
Country
Afghanistan
Bangladesh
BhutanBrunei
Cambodia
China
East Timor
Hong Kong
India
Indonesia
Japan
Laos
Macao
Malaysia
Maldives
Myanmar
Nepal
North Korea
Pakistan
Philippines
Singapore
South Korea
Sri Lanka
TaiwanThailand
Vietnam
Total

Annual Fire counts
248
3113
209
35
25360
56668
1128
69
63696
60224
1771
32601
0
4515
0
68279
2230
1772
6748
4926
7
578
1277
307
31643
15051
382455% contribution
0.065
0.814
0.055
0.009
6.631
14.8170.295
0.018
16.65415.747
0.463
8.524
0.000
1.181
0.000
17.853
0.583
0.463
1.764
1.288
0.002
0.151
0.334
0.080
8.274
3.935
100

The cluster analysis reveals that the fire hotspots arelocated in the northwestern regions of India, comprising
the Indo-Gangetic plains, a hotspot of agriculturalresidue burning events. Fire clusters in central India
covering Uttar Pradesh and Chattisgarh, including
southern Andhrapradesh were associated with tropicaldeciduous forest burning, whereas fire clusters in parts ofTamilnadu, Karnataka (southern India), Assam andMizoram (northeastern India) were attributed to evergreen forest burning. A relatively large cluster of fires
was noted in the northeastern Yunnan region of China
spreading towards the south with extensive but relatively
small fire clusters in Myanmar, Laos, Cambodia, andVietnam, continuing to Surat-Tani Province in Thailand,
covering a variety of ecosystems (Fig. 10). Clusters offires were also observed in Riau, Sumatra, Kalimantan(Indonesia) and East-Timur, some of which were associated with peatland burning. Fires in Abra, Apayo of thePhilippines were linked to slash and burn, including pine
forest burning in the mountain provinces. Fires extending
from Hainan Province in the lower northeast of China up
to Jilin Province in the upper northeast can also be seen in
Fig. 10, along with other fires mainly attributed toFig. 6 Monthly variation in fire occurrences in the Asian region.

Fire Counts
Aqua
Terra

Fig. 7 Monthly variation in Aqua and Terra MODIS fire occurrences in the Asian region.

Vegetation Fires in the Asian Region

Fig. 8 MERIS-derived (300m) vegetation map of the Asian region.

Fig. 9 Percentages of fire occurrences in various vegetation categories.

Fig.10 Fire clusters in the Asian region for the year 2005. The K-means algorithm has beenused to detect the fire clusters and the hotspot areas in the region.

71

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K. P. VADREVU and C.O. JUSTICE

agricultural residue burning. The hotspot clusters of fire
events identified in this study may warrant further attention to assess the relationship between fires, environmental factors and risk.
The new knowledge on fire hotspots delineated in thisstudy can be used in a variety of ways, such as exploring
why fires are clustered and assessing the underlying
causative factors including fire-vegetation relationships,
fire-pollutant-aerosol interactions, etc. Hotspots identified at the provincial scale can help local governmental
authorities to intensify their remedial measures anddevelop future strategies for more effective fire control.
Further, as MODIS active fires can be accessed at
specific lat/longitudes, such information can be used forfire management and mitigation options.

4. Satellite Observational Needs and Priorities
for Improved Fire Research in the AsianRegionIn this section, we identify some of the importantneeds and priorities useful for advancing fire research in
the Asian region.
4.1 Algorithm development, calibration andvalidation
In tropical systems, fires have strong diurnal and seasonal cycles (Goldammer & Price, 1998; Giglio, 2007).
Geostationary systems provide the best opportunity for
capturing the diurnal cycle of fire activities; however,traditionally they have had a poor spatial resolution. In
contrast, polar orbiters have a higher spatial resolution,
but provide a limited sampling of the diurnal cycle of fire
activity. Thus, integrating geostationary and polar satellite data for effective fire monitoring should be exploredthrough data fusion algorithms (Li & Roy, 2000).
As indicated earlier, several remote sensing productsof fire counts, burned areas and fire radiative energy areavailable to the Asian fire research community, each with
its own potentials and certain limitations. Error sources
include detection bias, false alarm bias, cloud cover bias,
sampling and seasonal error bias, etc. (Giglio, 2007).
Although some of the recent satellites such as MODIS
under ideal conditions (free of clouds, smoke and sun
glint) can detect fire sizes of less than 50m2 (although thefoot print size of such a fire is 1 km2), such conditions
seldom exist in tropical regions, most importantly in Asia.
Cloud coverage in tropical areas can have a pronounced
effect, limiting fire detection. The impact of cloud
obscuration on the detection of active fires in tropical
Amazonia has been well documented and is suggested toaccount for an omission rate of approximately 11% of all
fires imaged by coarse resolution sensors (Schroeder
et al., 2008). In addition, areas of cloud shadow andsemitransparent clouds in tropical areas are also inferred
to affect the thermal properties of the surface, thereby
potentially enhancing their impact beyond the 11% estimate produced for the active fire data. Changes in thethermal contrast between adjacent areas with different

vegetation cover conditions can also influence commission errors (Shroeder et al., 2008). These factors may
also impact the fire radiative power retrieved from satellite active fire detections (Roberts & Wooster, 2008).
Understorey burning in tropical forests of the Asian
region is another issue. For example, most of the fires in
the forested regions of India were reported to be subsurface fires, with burning of litter rather than crown fires(Bahuguna & Upadhyay, 2002; Saha, 2002). Accurate
identification of such fires may be difficult. In such cases,
burn scar identification requires very high resolution data
(Li & Roy, 2000) and improved algorithms (Morton et al.,2011). In addition to those identified above, other local
factors may hinder their detection. For example, agricultural fires are short-lived and monitoring themrequires satellites with high revisit cycles. Further, due tothe small field sizes (less than five acres), validating boththe active fires as well as burned areas seems a challenge.
To address these issues on a priority basis, local researchers can aid in identifying product limitations, following extensive validation against ground-truth data as
well as inter-comparison of algorithms, focusing on
individual ecosystems and then extending to larger
biomes. In short, the above issues clearly suggest theneed for regionally fine-tuned algorithms for improving
the accuracy of active fire and burned area products.
4.2 International network of fire validation sites
There is a need for establishing a network of fire
validation sites in diverse ecosystems, including agricultural systems, to develop and to assess algorithm
sensitivities to different fire regimes in the Asian region.
4.3 Ground based data
There is a strong need for collection of ground-based
data on fire characteristics through field-based campaigns to include fuel loads, fuel moisture content,
combustion completeness, emission factors, etc., as theyvary based on the ecosystem type, local conditions andmeteorology. Including this variability may improve
trace gas and aerosol emission calculations and theresulting climate impacts (van Der Werf et al., 2006).
4.4 Data availability and accessibility
There is a clear need for space agencies and national
governments to increase their international coordination
activities with respect to sharing locally available data onland use/cover, meteorology, fire observations, vegetation, etc. (Justice et al., 2003). In the Asian region, several countries (India, China, Japan, Singapore, Malaysia,etc.) over a period of years have already developed robust
remote sensing laboratories to acquire and process data
and develop products. Coordination amongst different
Asian countries is needed to make the data freely available and accessible to the public through a common
platform. Globally, satellite observations are currently
being coordinated through the Coordination Group for
Meteorological Satellites (CGMS) and the Committee on
Earth Observation Satellites (CEOS). Input to space

Vegetation Fires in the Asian Region

agencies from the scientific and satellite data user community is being coordinated through the GOFC-GOLD
Program and associated regional fire networks. The
Global Fire Monitoring Center, Germany, has taken thelead in coordinating the broader needs of the fire monitoring community in the context of the United Nations
International Strategy for Disaster Reduction (UNISDR)
Global Wildland Fire Network. Validation protocols forsatellite fire products and standards for validation
reporting are being developed by the CEOS Land
Product Validation subgroup.
4.5 Data assimilation system for early warning offires
For developing a robust early warning system relating to fire danger, including post-fire impacts, there is astrong need for developing a data assimilation system for
the Asian region, which can ingest both ground-based
and remotely-sensed fire information. Fire danger rating
is a means of quantifying the potential or ability of a fireto start, spread and cause damage (Merrill & Alexander,
1987). Formal Fire Danger Rating Systems (FDRS)
have been in development in Canada, Australia and theUnited States for about 75 years (Stocks et al. 1989). In
Indonesia and Malaysia, a fire danger rating system has
been developed, involving the Canadian International
Development Agency (CIDA), the Canadian Forest
Service (CFS) and a number of local government agencies and universities (de Groot et al., 2006). The
Canadian Forest Fire Danger Rating System (Stocks et al.
1989) has two subsystems that are currently used
operationally: the Canadian Forest Fire Weather Index(FWI) System (Van Wagner, 1987) and the Canadian
Forest Fire Behavior Prediction (FBP) System (Forestry
Canada Fire Danger Group, 1992). Risk, weather, fuels,
and topography provide the necessary inputs to predictfire weather, fire occurrences and fire behavior. Fuelmoisture models are currently being developed for arange of Canadian forest types. Together, these systems
can predict the potential fire danger within the forest. To
develop and extend such a system for the entire Asian
region, there is a strong need for the individual countries
to form a coalition and share data on a common platform
for building a data assimilation system useful for detecting fire danger. Such a system can provide a valuable
resource for organizing and integrating scientific knowledge for addressing fire and transboundary air pollution
problems in the region. In addition to the above, to meet
local and regional user needs, data management issues,
including data sharing policies, should be evaluated andrefined as necessary.
Several of the above needs can be addressed via
expert interaction through international programs, suchas GOFC-GOLD, which has been in existence for almost
fourteen years. This program’s details, goals and implementation strategies are highlighted below, and we callfor increased participation of Asian researchers throughnetworking with the GOFC program.

73

5. GOFC-GOLD Fire
GOFC-GOLD is an organization focused on international coordination of enhanced Earth observations. Its
overall aim is to improve the quality and availability ofspace-based and in-situ observations at regional andglobal scales and to encourage the production of appropriate, timely and validated information products. Originally developed as a pilot project by the Committee on
Earth Observation Satellites (CEOS) during 1997 as partof their Integrated Global Observing Strategy (Ahern
et al., 1998), GOFC-GOLD is now a panel of the Global
Terrestrial Observing System (GTOS). The essence of
the GOFC-GOLD implementation strategy is to develop
and demonstrate operational monitoring at regional andglobal scales by conducting pilot projects and developing
prototype products under three different themes: land
cover characterization and change, fire mapping andmonitoring, and biophysical processes (Townshend et al.,2004).
The GOFC- GOLD Fire Mapping and Monitoring
Implementation Team (Fire IT) is comprised of experts
from national and international space agencies, governmental and non-governmental environmental organizations, and universities. Fire IT aims to refine andarticulate international observation requirements andencourage the use of satellite-derived fire products andinformation from existing and planned systems for globalchange research, fire management and policy decisionmaking. This includes identifying the observation
priorities and needs of the fire community, such as
facilitation of collaborative research in recognized priority areas, periodically identifying critical observation
gaps (Justice et al., 2003), promoting the use of spaceborne assets for fire research, provision and validation offire products, and improving data distribution andcapacity building. The current GOFC-Fire IT goals andsub-tasks are highlighted in Fig. 11.
More specifically, the function of Fire IT includes:• Developing a connected fire observation community
with a common agenda for advancing community
goals.
• Refining and articulating the international requirements for fire related observations.
• Promoting free and open provision of high-quality
derived-data fire products to meet both science andapplication user needs.
• Increasing access to and encouraging best use of fireproducts from existing (and future) satellite observing
systems, for fire management, policy decisionmaking and global change research.
• Promoting the provision of long-term, systematic
satellite observations necessary for the production of
the full suite of recommended fire products and thedevelopment of new technologies for fire management.
Fire IT is actively pursuing these goals and functions

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K. P. VADREVU and C.O. JUSTICE

Fig. 11 GOFC-Fire IT goals and strategies.

through international and national contributory projects
involving regional experts and strategic partnerships withthe relevant international organizations. Currently, experts from several regional networks are involved with
the GOFC-Fire IT program, covering diverse regions
such as the Amazon, northern Eurasia, Central Asia,
central Africa and West Africa. Also included are southern Africa, western Africa, Latin America, Southeast
Asia and India. GOFC-Fire IT is continuously expanding
its outreach activities all over the world and strongly
welcomes the participation and interaction of Asian
researchers.

Acknowledgements
We thank Dr. Haruo Tsuruta, University of Tokyo,
Japan, for inviting us to contribute this article.
References
Agee, J.K. (1994) Fire in our future. International Journal of
Ecoforestry, 10: 184-193.
Andreae, M.O. and P. Merlet (2001) Emission of trace gases andaerosols from biomass burning. Global Biogeochemical Cycles,
15: 955-966.
Ahern, F.J., A. Belward, P. Churchill, R. Davis, A. Janetos, C.

Justice, T. Loveland, J-P. Malingreau, M. Maiden, D. Skole, V.
Taylor, Y. Yasuoka and Z. Zhu (1998) A Strategy for Global
Observation of Forest Cover, Canada Center for Remote Sensing, Ottawa, Ontario, Canada, GOFCTP-2.
Akimoto, H. (2003) Global air quality and pollution. Science,
302(5651): 1716-1719.
Badarinath, K.V.S., S.K. Kharol, K.M. Latha, T.R.K. Chand, V.K.
Prasad, A.N. Jyothsna and K. Samantha (2007) Multiyear
ground-based and satellite observations of aerosol properties
over a tropical urban area in India. Atmospheric Science Letters,
8: 7-13.
Bahuguna, V.K. and A. Upadhyay (2002) Forest fires in India:
policy initiatives for community participation. International
Forestry Review, 4(2):122-127.
Barber, C.V. and J. Schweithelm (2000) Trial By Fire: Forest Fires
and Forestry Policy in Indonesia’s Era of Crisis and Reform,
World Resources Institute.
<http://www.wri.org/ publication/trial-by-fire>
Boschetti, L. and D.P. Roy (2009) Strategies for the fusion ofsatellite fire radiative power with burned area data for fireradiative energy derivation. Journal of Geophysical Research,
114: D20302, doi:10.1029/2008JD011645.
Bicheron, P., A. Amberg, L.Bourg, D. Petit, M. Huc, B. Miras, C.
Brockmann, S. Delwart, F. Ranéra, O. Hagolle, M. Leroy and O.
Arino (2008) Geolocation Assessment of 300 m resolution
MERIS GLOBCOVER Ortho-Rectified Products.
<http://postel.mediasfrance.org/IMG/pdf/Bicheron_etal_MERI
SColloque_sept2008.pdf>
Brauer, M. (1999) Health impacts of biomass air pollution. In: K.T.
Goh, D. Schwela, J.G. Goldammer and O. Simpson, eds., HealthVegetation Fires in the Asian Region
Guidelines for Vegetation Fire Events – Background Papers,
pp.189-254, Singapore: World Health Organization.
Butchaiah, G., S. Bonnet, C. Menke and S. Garivait (2009) Air
pollutant emissions from rice straw open field burning in India,
Thailand and the Philippines. Environmental Pollution, 157 (5):1554-1558.
Carbopeat (2011) <http://www.geog.le.ac.uk/carbopeat/>Csiszar, I.A., O. Arino, R. Geraci, L. Giglio, J.G. Goldammer, W.
de Groot, C.O. Justice, S. Kondragunta, E. Prins, R. Sessa and K.
Tansey (2009) Fire Disturbance. Global Terrestrial Observing
System. GTOS Secretariat NRL, Food and Agriculture Organization of the United Nations (FAO).
<http://www.fao.org/gtos/ doc/ECVs/T13/T13.pdf>
Chan, L.Y., C.Y. Chan, H.Y. Liu, S. Christopher, S.J. Oltmans and
J.M. Harris (2000) A Case study on the Biomass Burning inSoutheast Asia and Enhancement of Tropospheric Ozone overHong Kong. Geophysical Research Letters, 27(10):1479-1482.
Christopher, S.A., J. Chou, R. M. Welch, D.V. Kliehea and V.S.
Connors (1998) Satellite investigations of fire, smoke andcarbon monoxide during April 1994 MAPS mission: Casestudies over tropical Asia. Journal of Geophysical Research,
103:19327-19336.
DeBano, L.F., D.G. Neary and P.F. Ffolliott (1998) Fire’s Effects on
Ecosystems, New York, Wiley.
de Groot, W., R.D. Field, M.A. Brady, O. Roswintiarti and M.
Mohamad (2006) Development of the Indonesian and Malaysianfire danger rating systems. Mitigation and Adaptation Strategies
for Global, Change, 12:165-180.
Dennis, R.A., J. Mayer, G. Applegate, U. Chokkalingam, C.J.P.
Colfer, I. Kurniawan, H. Lachowski, P. Maus, R.P. Permana,
Y. Ruchiat, F. Stolle, Suyanto and T.P. Tomich (2005) Fire,
people and pixels: Linking social science and remote sensing tounderstand underlying causes and impacts of fires in Indonesia.
Human Ecology, 33: 465-504.
Dozier, J. (1981) A method for satellite identification of surface
temperature fields of subpixel resolution. Remote Sensing ofEnvironment, 11:221-229.
Forestry Canada Fire Danger Group (1992) Development andStructure of the Canadian Forest Fire Behavior Prediction
System, Ottawa, ON, For Can, Rep ST-X-3.
Fox J., D. Truong, A.T. Rambo, N.P. Tuyen, L.T. Cuc and S. Leisz(2000) Shifting cultivation: A new old paradigm for managing
tropical forests. BioScience, 50 (6): 521-528.
Giglio, L., J. Descloitres, C.O. Justice and Y. Kaufman (2003) An
enhanced contextual fire detection algorithm for MODIS.
Remote Sensing of Environment, 87: 273-282.
Giglio, L. (2007) Characterization of the tropical diurnal fire cycle
using VIRS and MODIS observations. Remote Sensing ofEnvironment, 108(4): 407-421.
Giglio, L., I. Csiszar, A. Restas, J.T. Morisette, W. Schroeder, D.
Morton and C.O. Justice (2008) Active fire detection and characterization with the Advanced Spaceborne Thermal Emission
and Reflection Radiometer (ASTER). Remote Sensing ofEnvironment, 112: 3055-3063.
Giglio, L., T. Loboda, B. Quayle, D.P. Roy and C.O. Justice (2009)
An active-fire based burned area mapping algorithm for the
MODIS sensor. Remote Sensing of Environment, 113(2):408-420.
Goldammer, J.G. (1990) Fire in Tropical Biota: Ecosystem Processes and Global Challenges, Springer-Verlag, New York.
Goldammer, J.G. and S. Price (1998) Potential impacts of climatechange on fire regimes in the tropics based on MAGICC and a
GISS GCM-derived lighting model. Climatic Change, 39:
273-296.
Gupta, P., S. Sahai, N. Singh, C. Dixit, D. Singh, C. Sharma, M.
Tiwari, R. Gupta and S. Garg (2004) Residue burning in
rice-wheat cropping system: causes and implications. Current
Science, 87(12): 1713-1717.
Hardy, C.C., K.M. Schmidt, J.M. Menakis and N.R. Samson

75(2001) Spatial data for national fire planning and fuel management. International Journal of Wildland Fire, 10: 353-372.
Hooijer, A., M. Silvius, H. Wösten and S. Page (2006) PEAT-CO2,
Assessment of CO2 emissions from drained peatlands in SE
Asia. Delft Hydraulics Report, Q3943.
Jaenicke, J., J.O. Rieley, C. Mott, P. Kimman and F. Siegert (2008)
Determination of the amount of carbon stored in Indonesian
peatlands, Geoderma, 147: 151-158.
Johnson, L.A. and P. Dearden (2009) Fire ecology and management of seasonal evergreen forests in Southeast Asia. In: M.
Cochrane, ed., Tropical Fire Ecology Climate Change, Land Useand Ecosystem Dynamics, pp. 289-310, Springer, jointly
published with Praxis Publishing, UK.
Justice, C.O., J.R.G. Townshend, E.F. Vermote, E. Masuoka, R.E.
Wolfe, N. Saleous, D.P. Roy and J.T. Morisette (2002) Anoverview of MODIS Land data processing and product status.
Remote Sensing of Environment, 83: 3-15.
Justice, C.O., R. Smith, M. Gill and I. Csiszar (2003) Satellitebased Fire Monitoring: current capabilities and future directions,International Journal of Wildland Fire, 12: 247-258.
Justice C.O., L. Giglio, D. Roy, L. Boschetti, I. Csiszar, D. Davies,
S. Korontzi, W. Schroeder and K. O’Neal (2011) Global Fire
Products from the MODIS instruments. In: B. Ramachandran,
C.O. Justice and M.J. Abrams, eds., Land Remote Sensing and
Global Environmental Change: NASA's Earth Observing Systemand the Science of ASTER and MODIS. Series: Remote Sensing
and Digital Image Processing, Vol. 11, Springer Verlag.
Kanungo, T., D.M. Mount, N.S. Netanyahu, C.D. Piatko, R.
Silverman and A.Y. Wu (2002) An efficient k-means clustering
algorithm: Analysis and implementation. IEEE Transactions on
Pattern Analysis and Machine Intelligence, 24: 881-892.
Lau, K. M. and K. M. Kim (2006) Asian monsoon anomalies
induced by aerosol direct effects. Climate Dynamics, 26,855-864, doi:10.1007/s00382-006-0114-z.
Li, Z. and D. Roy (2000) Towards a global system for forest firemonitoring and mapping: Break out recommendations andpriorities. In: F. Ahern, J.M. Gregoire and Chris Justice, eds.,
Forest Fire Monitoring and Mapping: A Component of Global
Observation of Forest Cover, Report of a Workshop, November
3-5, 1999. pp.49-51. European Commission, JRC,
MacQueen, J.B. (1967) Some methods for classification andanalysis of multivariate observations. Proceedings of 5-th
Berkeley Symposium on Mathematical Statistics and Probability,
Berkeley, 1: 281-297. University of California Press.
Merrill, D.F. and M.E. Alexander (1987) Glossary of Forest Fire
Management Terms, Fourth Edition, Ottawa, ON, National
Resource Council of Canada, NRCC No. 26516.
Morton, D.C., R.S. DeFries, C.J. Nagol, C.M. Souza, Jr., E.S.
Kasischke, G.C. Hurtt and R. Dubayah (2011) Mapping canopy
damage from understory fires in Amazon forests using annual
time series of Landsat and MODIS data. Remote Sensing ofEnvironment, 115(7): 1706-1720.
Murdiyarso, D., L. Lebel, A.N. Gintings, S.M.H. Tampubolon, A.
Heil, M. Wasson (2004) Policy responses to complex environmental problems: Insights from a science-policy activity ontransboundary haze from vegetation fires in Southeast Asia.
Journal of Agriculture, Ecosystems and Environment, 104:
47-56.
Page, S. E., F. Siegert, J. O. Rieley, H.-D.V. Boehm, A. Jaya and S.
Limin (2002) The amount of carbon released from peat andforest fires in Indonesia during 1997, Nature, 420,
doi:10.1038/nature01131.
Pyne, S.J., D.L. Andrews and P.L. Richard (1996) Introduction to
Wildland Fire, New York, John Wiley.
Radojevic, M. (2003) Chemistry of forest fires and regional hazewith emphasis on Southeast Asia. Pure and Applied Geophysics,
160:157-187.
Roberts, S.J. (2000)Tropical fire ecology. Progress in PhysicalGeography, 24(2): 281-288.

76

K. P. VADREVU and C.O. JUSTICE

Ramanathan, V. and G. Carmichael (2008) Global and regional
climate changes due to black carbon. Nature Geoscience, 1:
221-227.
Roberts, G. and M.J. Wooster (2008) Fire detection and firecharacterization over Africa using Meteosat SEVIRI. IEEE
Transactions on Geoscience and Remote Sensing, 48(4):1200-1219.
Roy, D.P., Y. Jin, P.E. Lewis and C.O. Justice (2005) Prototyping a
global algorithm for systematic fire-affected area mapping using
MODIS time series data. Remote Sensing of Environment, 97:
137-162.
Saha, S. (2002) Anthropogenic fire regime in a tropical deciduous
forest of central India. Current Science, 82: 101-104.
Sastry, N. (2000) Forest fires, Air pollution and Mortality in Southeast Asia, Labor and Population Program, Working Paper Series,
00-19. <http://www.Rand.org.>
Schroeder, W., I. Csiszar and J. Morisette (2008) Quantifying theimpact of cloud obscuration on remote sensing of active fires in
the Brazilian Amazon. Remote Sensing of Environment, 112 (2):456-470.
Schmidt-Vogt, D. (1998) Defining degradation: the impacts ofswidden on forests in northern Thailand. Mountain Researchand Development, 18: 135-149.
Siegert, F., G. Ruecker, A. Hinrichs and A.A. Hoffmann (2001)
Increased damage from fires in logged forests during droughts
caused by El Niño. Nature, 414: 437-440.
Stocks, B.J., B.D. Lawson, M.E. Alexander, C.E. Van Wagner, R.S.
McAlpine, T.J. Lynham and D.E. Dube (1989) Canadian forest
fire danger rating system: an overview. The Forestry Chronicle,
65: 258-265.
Smith, M., B. Jalaludin, J. Byles, L. Lim and S. Leeder (1996)
Asthma presentations to emergency departments in western
Sydney during the January 1994 Bushfires. International
Journal of Epidemiology, 25(6): 1227-1236.
Streets, D.G., K.F. Yarber, J.H. Woo and G.R. Carmichael (2003)
Biomass burning in Asia: Annual and seasonal estimates andatmospheric emissions. Global Biogeochemical Cycles, 17(4):1099, doi: 10.1029/2003GB002040.
Stolle, F, K.M. Chomitz, E. F. Lambin and T.P. Tomich (2003)
Land use and vegetation fires in Jambi Province, Sumatra,Indonesia. Forest Ecology and Management, 179(1-3): 277-292.
Townshend, J.R.G., C.O. Justice, D.L. Skole, A. Belward, A.
Janetos, I. Gunawan, J. Goldammer and B. Lee (2004) Meeting
the goals of GOFC: an evaluation of progress and steps for the
future. In: G. Gutman et al., eds., Land Change Science, pp.
31-52, Kluwer Academic Publishers, Dordrecht, Netherlands.
Vadrevu, K.P., E. Ellicott, K.V.S. Badarinath and E. Vermote(2011) MODIS derived fire characteristics and aerosol opticaldepth variations during the agricultural residue burning season,
north India. Environmental Pollution, 159: 1560-1569.
Van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, P.S.
Kasibhatla and A.F. Arellano, Jr. (2006) Interannual variability
on global biomass burning emissions from 1997 to 2004.
Atmospheric Chemistry and Physics, 6: 3423-3441.
Venkataraman, C., G. Habib, D. Kadamba, M. Shrivastava, J.F.
Leon, B. Crouzille, O. Boucher and D.G. Streets (2006) Emissions from open biomass burning in India: integrating theinventory approach with the higher resolution Moderate
Resolution Imaging Spectroradiometer (MODIS) active fire andland count data. Global Biogeochemical Cycles, 20: GB2013,
doi: 10, 1029/2005GB002547.

VanWagner, C.E. (1987) Development and structure of theCanadian forest fire weather index system, Forestry Technical
Report, 35, Canadian Forestry Service.
Vermote, E., E. Ellicott, O. Dubovik, T. Lapyonok, M. Chin, L.
Giglio and G.J. Roberts (2009) An approach to estimate global
biomass burning emissions of organic and black carbon from
MODIS fire radiative power. Journal of Geophysical Research,
114, doi:10.1029/2008jd011188.
Wooster, M.J., B. Zhukov and D. Oertel (2003) Fire radiative
energy for quantitative study of biomass burning: derivationfrom the BIRD experimental satellite and comparison to
MODIS fire products. Remote Sensing of Environment, 86:
83-107.

Krishna PRASAD VADREVU
Dr. Krishna PRASAD VADREVU is working as an
Associate Research Professor at the Geography
Department, University of Maryland, College
Park, USA. His research interests include satellite
remote sensing, biomass burning, and greenhousegas emission inventories. His Ph.D. work focusedon biodiversity and forest cover studies involving remote sensing
techniques. At the University of Maryland, USA, he is currently focusing
on fire research. He is also the Fire Implementation Team Officer for the
GOFC-GOLD program, which is global in scope. He has published oversixty research papers in international and national journals.

Christopher O. JUSTICE
Professor JUSTICE received his Ph.D. from the
University of Reading, United Kingdom. Since
2001 he has been a professor and research director
in the Geography Department of the University of
Maryland, College Park, USA. He is a teammember and land discipline chair of the NASA
Moderate Imaging Spectroradiometer (MODIS) Science Team and is
responsible for the MODIS Fire Product and the MODIS Rapid Response
System. He is a member of the NASA NPOESS Preparatory Project (NPP)
Science Team. He is Co-Chair of the GOFC/GOLD-Fire Implementation
Team, a project of the Global Terrestrial Observing System (GTOS), and amember of the Integrated Global Observation of Land (IGOL) Steering
Committee. He is on the Strategic Objective Team for USAID's Central
Africa Regional Project for the Environment. He is Co-Chair of the GEO
Global Agricultural Monitoring Task. His current research is on land coverand land use change, the extent and impacts of global fire, global
agricultural monitoring, and the information technology and decision
support systems associated with these. He has published over 130 research
papers in international journals and is among the most highly citedresearchers in the world.

(Received 20 May 2011, Accepted 6 September 2011)